http://imagej.273.s1.nabble.com/Timelapse-Noise-Quantification-tp5009923p5009937.html
> Noise is a tricky subject - clearly if you know what the signal should be
> then you can measure the noise, but usually we don't.
> An alternative that we use in colocalization measurements is based on the
> idea that noise can be addressed by acquiring two images of the specimen-
> in the absence of noise and movement they should be identical. The
> magnitude of the noise can be displayed using a scattergram and quantified
> using the correlation between the two nominally identical images.
>
> Adler J., Bergholm F., Pagakis S.N., Parmryd I (2008)
> Noise and Colocalization in Fluorescence Microscopy: Solving a Problem.
> Microscopy & Analysis, Sept. 2008, 7-10.
>
> J. Adler, S.N. Pagakis and I. Parmryd (2008)
> Replicate Based Noise Corrected Correlation for Accurate Measurements of
> Colocalization
> J. Microscopy 230(1),121-133.
>
>
> ________________________________________
> From: ImageJ Interest Group [
[hidden email]] on behalf of Rebecca
> Keller [
[hidden email]]
> Sent: 06 October 2014 20:11
> To:
[hidden email]
> Subject: Timelapse Noise Quantification
>
> Dear Imagers,
>
> I have done some experiments adding Gaussian noise to my time lapses to
> determine the efficacy of a certain filter, and would like to give an idea
> of how much noise had been there originally. Any suggestions, then, for
> measuring this "baseline experimental noise?"
>
> I am thinking perhaps I should take a z-projected (well, "t-projected,"
> really) mean image of the series, subtract it from each image in the stack,
> then do a z-projected standard deviation, report the average value of this
> projected image? Or instead of subtracting the first mean image, maybe I
> should divide the stack by it?
>
> Any advice here would be welcome,
>
> Jacob Keller
>
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